Method and system of sentiment-based tokenization and secure deployment thereof

ABSTRACT

Method and system of generating and deploying a tokenized digital asset. The method includes monitoring generation of a sentiment expression in accordance with social media content data, the sentiment expression rendered in association with a sentiment community of interest, generating a sentiment block digital asset in accordance with the sentiment expression and rendering, as a tokenized digital asset, a digital token data file that is a unique representation of the sentiment block digital asset.

RELATED APPLICATIONS

This application is a continuation-in-part of, and claims benefit ofpriority to, U.S. patent application Ser. No. 18/107,714 filed Feb. 9,2023 which in turn claims benefit of priority to U.S. patent applicationSer. No. 17/204,324 filed Mar. 17, 2021 now issued as U.S. Pat. No.11,605,004 which in turn claims benefit of priority to U.S. patentapplication Ser. No. 16/216,038 filed Dec. 11, 2018 now issued as U.S.Pat. No. 11,030,533; the aforementioned priority application Ser. Nos.18/107,714, 17/204,324 and 16/216,038 are hereby incorporated in theirentirety herein.

TECHNICAL FIELD

The disclosure herein relates to tokenization and deployment of digitalassets based on sentiment generation.

BACKGROUND

Current techniques associate social connections of a user that can beused effectively for purposes such as promoting products and services orfor making personalized interactions in a system. Such approaches maylack a system that can collect and interpret necessary informationwithout limitations based on literal interpretations of sentimentinformation as collected, while respecting privacy and intellectualproperty rights of individuals regarding content as created.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates, in an example embodiment, a server-based system forgenerating a transitory sentiment community.

FIG. 2 illustrates, in one example embodiment, an architecture of acomputing system for generating a transitory sentiment community.

FIG. 3 illustrates, in an example embodiment, a linguistic frameworkincluding a sarcasm sentiment module.

FIG. 4 illustrates an example embodiment of pre-processing data ingenerating a transitory sentiment community.

FIG. 5 illustrates, in an example embodiment, a method of operation ingenerating a transitory sentiment community.

FIG. 6 illustrates, in an example embodiment, an architecture of acomputing system for initiating an interface concurrent with generationof a transitory sentiment community.

FIG. 7 illustrates, in an example embodiment, a method of operation forinitiating an interface concurrent with generation of a transitorysentiment community.

FIG. 8 illustrates, in an example embodiment, an architecture of acomputing system for generating a sentiment-based digital asset.

FIG. 9 illustrates, in an example embodiment, a scheme for generatingand deploying a sentiment-based digital asset.

FIG. 10 illustrates, in an example embodiment, a method of operation ingenerating a tokenized digital asset in accordance with asentiment-based digital asset.

FIG. 11 illustrates, in an example embodiment, a method of operation inrendering a tokenized digital asset.

DETAILED DESCRIPTION

Among other technical advantages and benefits, solutions provided hereinprovide a system that initiates, concurrent with execution of a softwareapplication that generates a transitory sentiment community at a servercomputing device, an interface at a subscriber computing device. Thetransitory nature of the sentiment community as generated ebbs andflows, while parallel initiating of a subscriber interface at thesubscriber computing device mirrors that ebb and flow. In accordancewith such ebb and flow, the sentiment community is created or deleted inaccordance with threshold sentiment parameters that define the creatingor deleting of the transitory sentiment community. In some embodimentsas suited for an ad hoc purpose, the transitory sentiment community maybe based on collective sentiment or emotion inferred from posted contentusing a linguistic framework, allowing for real-time, fluid monitoringof such sentiment community in accordance with its transitory nature,while respecting individual privacy rights associated with contentsources.

Embodiments herein further recognize a need for providing users controlof third party access and usage over their data content and intellectualproperty created, the data content configured in embodiments herein byway of sentiment blocks. Some embodiments herein provide for capturingsentiment-based content via sentiment blocks, tokenizing same, anddeploying to monetize user or content creator's intellectual property(IP) rights. In some aspects, embodiments herein enable monetizing IPrights over a blockchain infrastructure, also enabling payments directlyto users for their engagements via blockchain infrastructure. Also, along-standing problem of data and ID theft creates significantreputational and financial implications for companies who centralize thecollection and storage of user data. Clearly, current solutions ofcentralized data storage are challenged as both personal and behavioraldata significantly increases in volume worldwide. Centralized systemslike cloud storage do not mitigate this risk and in fact exacerbates thesituation even more as they become prime targets that attract data theftattempts.

In alleviation of the above problems, embodiments herein providesolutions by way of creating a distributed ledger of sentiment-relatedcontent created, with engagements for each user on a given network,returning and securing ownership of sentiments, comments, engagementexpressed anywhere on the public web to the particular individualcreating such expressive content. In particular, solutions providedallows each user to register their social forums, web forums and productor services reviews accounts. Once connected, solutions herein tracksall utterances and interactions of such registered users on the web, andkeeps an up to date accounting of sentiment, contexts and mentionedbrands, entities in a distributed ledger, the data being stored indistributed nodes across the globe on nodes such as laptops, phones andother handheld and wearable computing devices. This network continuouslygathers, processes and indexes engagement, sentiment and quantity ofchatter generated by any given user. Embodiments here also enable usersto build their online engagement profile and control what content isshared with external buyers looking to utilize the topic commentaries inassociation with related sentiments.

In accordance with a first example embodiment, a method of generatingand deploying a tokenized digital asset is provided. The methodcomprises monitoring generation of at least one sentiment expression inaccordance with social media content data, the sentiment expressionrendered in association with a sentiment community of interest,generating a sentiment block digital asset in accordance with thesentiment expression and rendering, as a tokenized digital asset, adigital token data file that is a unique representation of the sentimentblock digital asset.

In accordance with a second example embodiment, also provided is acomputing system, which may be a networked server computer, or asubscriber client computer deployed in an edge computing networkedarrangement, for generating and deploying a tokenized digital asset. Thecomputing system includes a processor and a memory storing instructionsexecutable in the processor. The instructions, when executed in theprocessor, cause the processor to perform operations includingmonitoring generation of at least one sentiment expression in accordancewith social media content data, the at least one sentiment expressionrendered in association with a sentiment community of interest,generating a sentiment block digital asset in accordance with the atleast one sentiment expression and rendering, as a tokenized digitalasset, a digital token data file that is a unique representation of thesentiment block digital asset.

Also provided is a non-transitory, computer readable memory storinginstructions executable in a processor device. The instructions, whenexecuted in the processor device, cause the processor to performoperations including monitoring generation of at least one sentimentexpression in accordance with social media content data, the at leastone sentiment expression rendered in association with a sentimentcommunity of interest, generating a sentiment block digital asset inaccordance with the at least one sentiment expression and rendering, asa tokenized digital asset, a digital token data file that is a uniquerepresentation of the sentiment block digital asset.

One or more embodiments described herein provide that methods,techniques, and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or morenon-transitory memory resources of the computing device.

Furthermore, one or more embodiments described herein may be implementedthrough the use of logic instructions that are executable by one or moreprocessors of a computing device, including a server computing device.These instructions may be carried on a computer-readable medium. Inparticular, machines shown with embodiments herein include processor(s)and various forms of memory for storing data and instructions. Examplesof computer-readable mediums and computer storage mediums includeportable memory storage units and flash memory (such as carried onsmartphones). A server computing device as described herein utilizesprocessors, memory, and logic instructions stored in the memory orcomputer-readable medium. Embodiments described herein may beimplemented in the form of computer processor-executable logicinstructions or programs stored on computer-readable memory mediums.

System Description

FIG. 1 illustrates, in an example embodiment, transitory sentimentcommunity logic module 105 hosted at server computing device 101, withincloud networked system 100 for software application execution withconcurrent signaling. While server computing device 101 may includetransitory sentiment community logic module 105 as depicted, it iscontemplated that, in alternate embodiments, computing devices 102 a-c,including desktop workstations, laptop computers, and mobile computingdevices 102 a-c in communication via network 104 with server 101, mayinclude one or more sub-modules or portions that constitute transitorysentiment community logic module 105 as embodied in computerprocessor-executable instructions stored within a non-transitorycomputer readable memory. Database 103 may be communicatively accessibleto server computing device 101 (also referred to as server 101 herein)and also to one or more of client computing devices 102 a-c (alsocollectively referred to herein as client computing device 102) viacommunication network 104.

Client computing devices 102 a-c may be configured to include clientinterface module 106 constituted of processor-executable instructions,allowing interaction by a user at client computing devices 102 a-c withresults generated from, or in conjunction with, transitory sentimentcommunity logic module 105 at server 101. In one embodiment, clientinterface module 106 includes processor-executable instructions or asoftware application downloadable from server computing device 101 andconfigured to operate in conjunction with transitory sentiment communitylogic module 105 to allow physical user interface representations of thesentiment community at client computing device 102. It is contemplatedthat, in alternate embodiments, client interface module 106 may includeone or more sub-modules or portions that constitute transitory sentimentcommunity logic module 105 as embodied in computer processor-executableinstructions stored within a non-transitory computer readable memory,and executable in a processor of client device 102.

FIG. 2 illustrates, in an example embodiment, an architecture of servercomputing device 101 hosting transitory sentiment community logic module105. Server computing device 101 may include processor 201,non-transitory computer readable memory 202 that includes transitorysentiment community logic module 105, display screen 203, inputmechanisms 204 such as a keyboard or software-implemented touchscreeninput functionality, and communication interface 207 for communicatingwith client devices 102 a-c via communication network 104.

Transitory sentiment community logic module 105 includes instructionsstored in memory 202 of server 101, the instructions configured to beexecutable in processor 201. Sentiment community logic module 105 maycomprise portions or sub-modules including database module 210, datapre-processing module 211, sentiment analysis module 212, sarcasmmodification module 213 and sentiment community generating module 214.

Database module 210, in an embodiment, includes instructions executablein processor 201 in association with database module 210 to receivedata, in a database memory associated with server computing device 101,the data extracted from a plurality of data or content sources.

Data pre-processing module 211, in an embodiment, includes instructionsexecutable in processor 201 to pre-process the extracted data orcontent, based on at least one of text character removal and textcharacter replacement, to provide pre-processed data that includes a setof keywords used in a descriptive manner.

Sentiment analysis module 212, in an embodiment, includes instructionsexecutable in processor 201 to perform a sentiment analysis on the setof keywords, based at least in part upon a training model, to: (i) aidentify a conformance to at least one sentiment classification of a setof sentiment classifications recognized by the training model, and (ii)derive a sentiment intensity rating associated with the conformance. Insome example embodiments, sentiment classifications or labels mayrepresent sentiments or emotions, in addition to a “sarcasm” or“sarcastic” sentiment or emotion, as any one or more of “anger”,“disgust”, “fear”, “joy”, “sadness”, “surprise”, “trust”,“anticipation”, “distrust”, “satisfied”, “dissatisfied”, “disappointed”,“delighted”. It is contemplated that descriptions, classifications,labels and representations of sentiments or emotions may not be limitedto only the latter examples, and furthermore, need not be limited to anyparticular form of, or conformance with, particular grammatical forms inaccordance with tenses, verbs, nouns, or adjectives.

Sarcasm modification module 213, in one embodiment, includesinstructions executable in processor 201 to modify the sentimentintensity rating associated with the at least one sentimentclassification of the sentiment classifications recognized by a trainingmodel upon detecting a sarcasm sentiment that is above a sarcasmsentiment likelihood threshold. The sarcasm sentiment may be associatedwith a statistical likelihood that the set of keywords pertain tosarcasm based in art on the author of the underlying content. In oneembodiment the sarcasm sentiment likelihood threshold may be expressedas a statistical confidence level threshold value, above which thresholdvalue a sarcasm sentiment is established as detected.

Sentiment community generating module 214, in one embodiment, includesinstructions executable in processor 201 to generate the transitorysentiment community based at least in part on the sentimentclassification and the modified sentiment intensity rating in accordancewith applying sarcasm sentiment module 213.

FIG. 3 illustrates, in an example embodiment, linguistic framework 301that includes author profile component 303, pre-processing component 304which includes author profile pre-processing functions, and sarcasmidentification component 305 of sarcasm sentiment module 213 formodifying a sentiment intensity rating associated with an identifiedsentiment class. The identified sentiment class may be one, or more thanone in alternate embodiments, of a set of sentiment or emotionclassifications recognized by training model 306.

Data source input component 302 provides input data to database module210 to receive data in memory 202 or a database memory 103 associatedwith server computing device 101, the data being extracted from aplurality of data sources. The data sources comprise publicly availabledata such as, but not limited to, tweet content, instant messagingcontent, product review content, service review content, social mediaposting content, and website content.

Data gathered from content sources may be stored, in an OrientDB opensource NoSQL graph database in one embodiment, allowing flexibility,scalability, rapid scaling of data, providing advantages in queryoptimization, including use of lightweight edges and indexes thatimproves the speed of data retrieval. Data objects may be stored asnodes, with corresponding relationships represented as edges. Each nodehas unique properties depending on the source, but most nodes sharecommon properties including user ID, create date, text, source,sentiment, category, and emotion parameters. Edges that connect thenodes show the relationship between nodes. Some edges have their ownproperties (regular edges) while others do not (lightweight edges).

General database optimization and tuning techniques may be applied tofurther improve database performance, such as indexing techniquesapplied to ensure advantageous balance between quick database access anddisk space, with lightweight edges incorporated to improve performanceand memory usage.

Feature engineering component 307 of linguistic framework 301 providesfeatures and operations to be applied upon pre-processed, or cleaned,data of data cleaning module 304.

FIG. 4 illustrates an example embodiment 401 in exemplary detail relatedto pre-processing data in generating a transitory sentiment community,showing pre-processing operations performed on content, including textremoval and text replacement operations.

In an embodiment that implements topic extraction operations, a topicmodel based on dirilecht distributions and multinomial may be used toprovide classification and general understanding of latent topics withinthe data content, such as a review of a product or service. In anembodiment, prior knowledge of topic-word distribution may be sued toprovide an initialization to the starter distributions when selectingtopic per word and to the data content item as well. This process ofinitializing the likelihood based on prior words may be incorporatedinto an online dirilecht-based technique. Creating the prior knowledgeof topic-word distribution may be done manually for each use case toprovide custom priors and allow for a guided latent Dirichlet allocation(LDA) approach which specializes in learning topics for a specificdomain. To further improve and to be able to re-incorporate the priorswe allow for constant reminder of prior information to stay within themodel through the alpha and beta parameters of the LDA model. These twoparameters in the online LDA algorithm allow the priors to maintainrelevant throughout the inference and training processes. To calculatean optimal alpha beta contribution factor of regression analysis may beused to calculate alpha and beta parameters separated by combinations ofprior knowledge and a set of coefficients.

Topic relevance ranking may be incorporated rank the words whichdescribe each latent topic. An after-training analysis of the model andcome with up relevancy and saliency scoring metrics. Adoption of thesemetrics allows for ranking of topic words, topic separation, and topicdominance. Attaching the new relevance metrics to online LDA with priorsprovide a unique and enhanced tool for topic analysis with qualitymeasures. For topic optimization, topic modeling methods generatemultinomial topic distribution in which each topic is a set of uni-gramtokens with a probability that determines how frequent each word appearsin a text. In order to have more understandable topics, uni-gram tokensmay be replaced with high quality phrases.

In one embodiment, a set of candidate labels may be extracted from thereference corpus. Then a relevancy score assigned to each candidatelabel. To score candidate labels, Kullback-Leibler divergence may beapplied to find similarity between distribution of words in a topic anddistribution of words in a label. Candidate labels may be ranked withrespect to each topic model and top ranked labels selected to label thecorresponding topic(s), a good label being denoted as having highsemantic relevance to the target topic, and low semantic relevance toother topics.

Next in regard to topic related sentiment or emotion analysis, apre-trained model 306 may be used to capture char embeddings is a newtactic in linguistic-based models. To further the model to highaccuracies, a specialized set of keywords may be identified and appliedto gather training data for each of a set of emotions or sentiments.Using the list of keywords for each emotion training data may beextracted from content sources. The data was cleaned prior to trainingto ensure the keywords were used in a descriptive manner. Additionally,trained models may be incorporated to implement a full artificialintelligence (AI)-based teacher-student model. Such AI teachers may beimplemented to select training data with high degree of confidence to beused to further train the emotion or sentiment model. To generatemeaningful phrases as candidate labels from reference collection,chunking parser methods may be used to extract noun phrases. Labelswhich contain more important words (higher probability) in the topicdistributions are considered as good labels. Multinomial distribution oftopics found with online LDA (with setting priors as guided LDA) may bethen matched with the top-ranked candidate labels which have highrelevance score to each individual topic.

Next with regard to the particular sentiment of sarcasm, sentiment-basedmodels may fail when sarcasm is involved in the input data content, forexample, in tweets. Sarcasm detection can help in gaining a betterinsight into customer sentiments. As incorporated into sarcasmmodification module model 213 for sarcasm sentiment detection andsentiment intensity modification, features may be analyzed from tweetsand their description in order to capture the sarcasm expressed intweets, for example. Further in reading to online content postingsincluding tweets, registered authors on have the opportunity to providea personal profile or self-description. Such self-description or authorprofile data may be used to extract information about personality traitsof content authors, to enable or help in classifying sarcastic tweetsfrom non-sarcastic ones with a high degree of accuracy. IN one exampleembodiment, a range of different feature-sets from tweet-text inconjunction with author's self-description may be engineer usingLinearSVM and Logistic Regression predictive models. Further,lexical-based features 307 of linguistic framework 301 may be generatedfrom review text such as n-grams, intensifiers, number of capital letterwords, binary feature for double quotes, emojis, and count ofpart-of-speech to name a few, as such features help in capturing thenuances of the writing style of a content author, such as Twitter users.Sentiment-based features may also be generated from the content.Positive, negative and difference in sentiment score, for example fromdifferent parts of the review text, are captured as features. Thisfeature enables detection of a degree of contrast of sentiment thatidentifies or captures a likelihood of existence of sarcasm.Topic-modeling based features may be captured from the user profiledescription by applying the topic modeling technique. Such feature isbased on the presumption that the author's self-description can give abetter idea of an inclination towards sarcasm.

In an embodiment, a slang related sentiment may be detected and appliedto modify, such as by amplifying or attenuating, the sentiment intensityrating upon detecting existence of a slang component of the set ofkeywords. To find slang sentiments, one or more slang lexicons, such asUrban Dictionary which is a web-based dictionary of slang definitions,lists of popular idioms, and SlangSD lexicon which contains slangs andtheir associated sentiments. If a slang term or component exists in thelexicon, the slang sentiment is collected, including collecting reviewscontaining the slang with high relevance score and the slang examplescontaining the slang from the slang lexicon source. In social media,slangs may be commonly used in short forms, and informal terms. Hence,slang polarity may be identified before sentiment classification ofreviews or blogs and other content. Thus, slangs or idioms may bedetected in reviews and other content, and a sentiment score assigned toeach slang term. Such results may be aggregated based on a highestranking to produce a unique sentiment score for each slang. Since slangsusage and definition may change over time and in different data sources,the data collection of slangs may be updated continuously.

Upon preprocessing the input content, steps applied to detect slangs incontent may include a lookup table search in conjunction with the slanglexicon sources, then filtering out all non-slang words from text tofind single-words slangs, such as by using WordNet and English wordslexicons. Then, single words fond may be cross-referenced any existingdefinition in the slang lexicon source, upon which they are detected asslang, or otherwise simply a mis-spelling. Then upon slang detection,determining whether the slang appearing in the data content isconsistent with its usage pattern in the lexicon source. In other words,if a slang in a review, for example, is consistent with its definitionfrom Urban Dictionary as lexicon source. A relevancy score may beassigned to each content item containing the slang. In each content itemthat contains a slang, the slang is removed and compared to rest of thewords in a text with the slang related words. This comparison is basedon the semantic similarity between words in the content and words in theslang related words, for example, based on word2vec embeddings trainedon Google News. If a word had a high semantic similarity to the relatedwords, it may be added to a related words list. After comparing a reviewtext with the slang related words, a list of floating numbers may bedetermined to represent a semantic distance between each pair of words.To assign the relevancy score to each data content item, the maximumsemantic distance may be selected.

Methodology

FIG. 5 illustrates, in an example embodiment, method 500 of generating atransitory sentiment community, method 500 being performed by one ormore processors 201 of server device 101. In describing the example ofFIG. 5 , reference is made to the examples of FIG. 1 through FIG. 4 forpurposes of illustrating suitable components or elements in performing astep or sub-step being described.

Examples of method steps described herein relate to the use of servercomputing device 101 including transitory sentiment community logicmodule 105 for implementing the techniques described. According to oneembodiment, the techniques are performed by server computing device 101in response to the processor 201 executing one or more sequences ofsoftware logic instructions that constitute transitory sentimentcommunity logic module 105. In embodiments, transitory sentimentcommunity logic module 105 may include the one or more sequences ofinstructions within sub-modules including database module 210, datapre-processing module 211, sentiment analysis module 212, sarcasmmodification module 213 and sentiment community generating module 214.Such instructions may be read into memory 202 from machine-readablemedium, such as memory storage devices. In executing the sequences ofinstructions contained in database module 210, data pre-processingmodule 211, sentiment analysis module 212, sarcasm modification module213 and sentiment community generating module 214 of transitorysentiment community logic module 105 in memory 202, processor 201performs the process steps described herein. In alternativeimplementations, at least some hard-wired circuitry may be used in placeof, or in combination with, the software logic instructions to implementexamples described herein. Thus, the examples described herein are notlimited to any particular combination of hardware circuitry and softwareinstructions.

Additionally, it is further contemplated that in alternativeembodiments, the techniques herein, or portions thereof, may bedistributed between client, or subscriber, computing devices 102 a-c andserver computing device 101. For example, computing devices 102 a-c mayperform some portion of functionality described herein with regard tovarious modules of which transitory sentiment community logic module 105is comprised, and transmit data to server 101 that, in turn, performs atleast some portion of the techniques described herein.

At step 510, processor 201 executes instructions in association withdatabase module 210 to receive data in memory 202 or a database memory103 associated with server computing device 101, the data beingextracted from a plurality of data sources.

In some embodiments, the data sources comprise publicly available dataconstituted of such as, but not limited to, tweet content, instantmessaging content, product review content, service review content,social media posting content, and website content.

At step 520, processor 201 executes instructions of data pre-processingmodule 211 to pre-process the data, based on at least one of textcharacter removal and text character replacement, to providepre-processed data that includes a set of keywords used in a descriptivemanner.

At step 530, processor 201 executes instructions of sentiment analysismodule 212 to perform a sentiment analysis on the set of keywords basedat least in part upon a training model, the sentiment analysisidentifying: (i) a conformance to at least one sentiment classificationof a set of sentiment classifications recognized by the training model,and (ii) a sentiment intensity rating associated with the conformance tothe at least one sentiment classification.

In an embodiment, the sentiment classification comprises a subset of theset of sentiment classifications as recognized by the training model.

In one variation, the method further includes identifying a plurality oftopics based on the pre-processed data and selecting at least one topicbased on a ranking of the plurality of topics.

A commonality or frequency of occurrence of some or all keywords formingthe set may predominantly pertain to the selected topic in accordancewith the ranking.

In another embodiment, the sarcasm sentiment is detected based at leastin part on a personality trait of an author of the data from which theset of keywords is sourced or is traceable, and the pre-processingfurther comprises pre-processing data of the personality trait of theauthor, to detect a sarcasm sentiment likelihood factor. The sarcasmsentiment likelihood factor may be expressed, or quantified, as astatistical confidence level, in one embodiment.

At step 540, processor 201 executes instructions of sarcasm modificationmodule 213 to modify the sentiment intensity rating associated with theat least one sentiment classification upon detecting a sarcasm sentimentparameter that is above a sarcasm sentiment likelihood threshold value.

In an embodiment, identifying the sentiment intensity rating includesdetermining a quantifiable statistical sentiment score associated withthe sentiment classification, and the modifying includes eitheramplifying and attenuating the sentiment intensity rating of thesentiment classification.

The instructions of sarcasm modification module 213 may be furtherexecutable to modify or alter the sentiment classification to one ormore alternate sentiment classifications recognized by the trainingmodel. In a further embodiment, altering the sentiment classificationalso includes replacing the sentiment classification with a generallycontrary or opposite sentiment classification when sarcasm sentiment isdetected above a certain threshold statistical confidence level. By wayof illustration and examples, where the sentiment classification relatedto a product or services review as expressed is originally interpretedas “delighted”, then a generally contrary or opposite sentimentclassification applied in generating the sentiment community in lieu of“delighted” might be “disappointed”; or where the sentimentclassification as expressed is originally interpreted as “satisfied”,then a generally contrary or opposite sentiment classification appliedin generating the sentiment community in lieu of “satisfied” might be“dissatisfied”; or where the sentiment classification as expressed isoriginally interpreted as “trust”, a generally contrary or oppositesentiment classification applied in generating the sentiment communityin lieu of “trust” might be “mistrust” or “distrust”.

In an embodiment, the method further comprises modifying the sentimentintensity rating by either amplifying, or attenuating, the sentimentintensity rating upon detecting existence of a slang component of theset of keywords.

At step 550, processor 201 executes instructions of sentiment communitygeneration module 214 to generate the transitory sentiment communitybased at least in part on the at least one sentiment classification andthe modified sentiment intensity rating.

In one variation, generating the transitory sentiment community may bebased on applying a sentiment intensity threshold to the modifiedsentiment intensity rating, whereupon the generating is initiated uponthe sentiment intensity rating crossing above, or below in anotherembodiment, the modified sentiment intensity threshold.

In an embodiment, the sentiment classification comprises a subset of theset of sentiment classifications, and the method further comprisesgenerating the transitory sentiment community based on a sentimentintensity threshold associated with respective ones of the subset ofsentiment classifications.

In some variations, the transient sentiment community, includingphysical user interface representations thereof, may be generated bysentiment community generating module 114 in conjunction with clientinterface module 106 of client computing device 102 at a displayinterface or a user interface of client computing device 102.

FIG. 6 illustrates, in an example embodiment, an architecture of aserver computing system 601 for initiating, concurrent with execution ofa software application that generates a transitory sentiment community,an interface for the transitory sentiment community. Although notdepicted in FIG. 6 , it contemplated that in some embodiments,transitory sentiment community interface module 605 of serverarchitecture 601 incorporates or subsumes the processor-executableinstructions of which transitory sentiment community logic module 105 isconstituted, including any or all of sub-modules database module 210,data pre-processing module 211, sentiment analysis module 212, sarcasmmodification module 213 and sentiment community generating module 214.

Transitory sentiment community interface module 605 may be constitutedof instructions executable in processor 201 of server computing device101 and include portions or sub-modules including sentiment parameterspecification module 610, sentiment task automaton module 611,concurrent execution monitoring module 612 and subscriber interfaceinitiating module 613.

Sentiment parameter specification module 610, in one embodiment,includes instructions executable in processor 201 to receive, at amemory 202 of the server computing device 101, a specificationidentifying at least one sentiment parameter in association with thesubscriber computing device, the at least one sentiment parameter beingrelated to an expected result generated during execution of a transitorysentiment community generation software application in a processor ofthe server computing device 101.

Sentiment parameters 610 a may include the various sentimentclassifications as used to generate of the transitory sentimentcommunity, for example as described in accordance with the exampleoperations of FIG. 5 .

Threshold Conditions 610 b relate to threshold conditions and values tobe applied to the sentiment parameters 610, including sentimentintensity values for quantifying or estimating the strength of thesentiment or emotion being expressed in the data content aspre-processed, in one embodiment. The threshold value or condition asset for a particular parameter may serve as a constraint, which onceexceeded, triggers or initiates generating a sentiment community basedon that particular sentiment. In some embodiments, certain sentiments oremotions may be agglomerated into a defined set, and a threshold valueset for the agglomeration of that set of sentiments. For example, a“positive” set may be defined to be constitute or agglomerate emotionsor sentiments such as “happy”, “satisfied”, “contented”, while a“negative” set may be constituted of an agglomeration of sentiments suchas “dissatisfied”, “sarcastic”, “disappointed”.

Subscriber computing devices 610 c may be pre-identified and specifiedas particular ones of client computing devices 102 selected, permittedor authorized to receive, view, and interact with results pertaining toexecution of a transitory sentiment community generation softwareapplication in processor 201 of server computing device 101.

Sentiment task automaton module 611, in one embodiment, includesinstructions executable in processor 201 to generate, by the processorduring execution of the sentiment community generation softwareapplication, a sentiment parameter task automaton representative of theat least one sentiment parameter;

Concurrent execution monitoring module 612, in one embodiment, includesinstructions executable in processor 201 to monitor, by the sentimentparameter task automaton, during concurrent execution with object codeof the sentiment community software application in the processor, theexpected result related to the at least one sentiment parameter;

Subscriber interface initiating module 613, in one embodiment, includesinstructions executable in processor 201 to initiate, at the subscribercomputing device 102, a subscriber interface that includes at least anotification of the at least one sentiment parameter in accordance withthe monitoring during the concurrent execution. In an embodiment, thenotification at subscriber computing device 102 is triggered byprocessor 201 of server device 101 when a pre-existing threshold valueset for a sentiment parameter, or an agglomeration of a set of sentimentparameters, is exceeded.

FIG. 7 illustrates, in an example embodiment, a method of operation 700for initiating, concurrent with execution of a software application thatgenerates a transitory sentiment community, an interface for thetransitory sentiment community. In describing the example of FIG. 7 ,reference is made to the examples of FIG. 1 through FIG. 6 for purposesof illustrating suitable components or elements in performing a step orsub-step being described.

Examples of method steps described herein relate to the use of servercomputing device 101 including transitory sentiment community logicmodule 105 and transitory sentiment community interface logic module 605for implementing the techniques described. According to one embodiment,the techniques are performed by server computing device 101 in responseto the processor 201 executing one or more sequences of software logicinstructions that constitute transitory sentiment community logic module105 and transitory sentiment community interface logic module 605, orany portions thereof. In embodiments, transitory sentiment communitylogic module 105 transitory sentiment community interface logic module605 may include instructions may be read into memory 202 frommachine-readable medium, such as memory storage devices, executable byprocessor 201 in performing the process steps described herein. Inalternative implementations, at least some hard-wired circuitry may beused in place of, or in combination with, the software logicinstructions to implement examples described herein. Thus, the examplesdescribed herein are not limited to any particular combination ofhardware circuitry and software instructions.

Additionally, it is further contemplated that in alternativeembodiments, the techniques herein, or portions thereof, may bedistributed between client, or subscriber, computing devices 102 a-c andserver computing device 101. For example, computing devices 102 a-c mayperform some portion of functionality described herein with regard tovarious modules of which transitory sentiment community logic module 105and transitory sentiment community interface logic module 605 arecomprised, and transmit data to server 101 that, in turn, performs atleast some portion of the techniques described herein.

At step 710, receiving, at a memory 202 of the server computing device101, a specification identifying at least one sentiment parameter inassociation with the subscriber computing device, the at least onesentiment parameter being related to an expected result generated duringexecution of a transitory sentiment community generation softwareapplication in a processor 201 of the server computing device 101.

In an embodiment, the transitory sentiment community softwareapplication executes in the processor 201 based on input data contentand pre-processing of data that pertains to at least one of a productreview and a services review.

At step 720, generating, by the processor 201 during execution of thesentiment community generation software application, a sentimentparameter task automaton, also referred to herein a sentiment taskautomaton, representative of the at least one sentiment parameter.

At step 730, monitoring, by the sentiment parameter task automaton,during concurrent execution with object code of the transitory sentimentcommunity software application in the processor 201, the expected resultrelated to the at least one sentiment parameter.

In an embodiment, the sentiment task automaton is defined by script codethat includes data identifying the subscriber computing device, the atleast one expected sentiment parameter and at least one of a thresholdvalue and a threshold condition of the at least one expected sentimentparameter.

In another embodiment, the monitoring comprises one or more applicationprogram interface (API) calls by the sentiment task automaton to thetransitory sentiment community software application during theconcurrent execution in processor 201 of server computing device 101.

At step 740, initiating, at the subscriber computing device 102, asubscriber interface that includes at least a notification of the atleast one sentiment parameter in accordance with the monitoring duringthe concurrent execution.

The size, intensity of sentiment(s), and rate of growth or subsidence ofthe transitory sentiment community, in one embodiment, may be used as aproxy for deciding when to initiate the notification to a displayinterface of the subscriber computing device. In a related variation,the notification may be presented thereon in the context of at least oneof a rate of growth, a rate of subsidence, and a benchmark size of asentiment community generated during the concurrent execution. Thebenchmark size may be set as a threshold, and may be user-established,such as in accordance with users at the client subscriber devices 102who are authorized to receive particular notifications, and optionally,charged with setting the threshold sizes applicable as a conditionprecedent to initiating the notifications. In another embodiment, thethreshold conditions or benchmark values may be set dynamically,depending on the users' tendency and history of timely responses tonotifications when based on certain sentiment communities.

In an embodiment, the initiating is based on the at least one expectedsentiment parameter exceeding a constraint in accordance with the atleast one of the threshold value and the threshold condition as set inaccordance with threshold values 610 b of sentiment parameterspecification module 610.

In yet another embodiment, the display user interface at clientsubscriber device 102 solicits a user input action thereon for at leastone of an approval, a dis-approval, an acknowledgement of thenotification of the at least one sentiment parameter, a replacement anda modification of the threshold value and the threshold condition of theat least one expected sentiment parameter.

In a further embodiment, data of the user input action, when performedat the display user interface of client subscriber device 102 inresponse to the solicitation, is transmitted to a memory of the servercomputing device 101. Depending on which, if any, sentiment communityattributes are of user concern or current user interest, in oneembodiment the users' input at client subscriber device 102 in responseto the notifications may update the threshold conditions 610 b atsentiment parameter specification module 610, allowing subsequentnotifications to be further customized to said user interest.

FIG. 8 illustrates, in an example embodiment, architecture 800 of acomputing system 101, 102 for generating a sentiment-based digitalasset. Architecture 800 may be embodied in server computing device 101.In edge computing embodiments, architecture 800 may be embodied insubscriber client device 102. In either configuration, sentiment-basedcontent as generated by a user of subscriber client device 102 may becaptured thereon as created by the user, and processed in parts usingtechniques disclosed herein, by subscriber client device 102 inconjunction with server computing device 101, with computing systemprocessor and memory resources being shared or dispersed variouslyamongst server computing device 101 and subscriber client device 102 forexecuting instructions constituting sentiment-based tokenizationapplication 805. In embodiments of architecture 800, processor 201 iscoupled to memory 202, display device 203, input mechanisms 204, and viacommunication interface 205, to immutable storage medium 806 viacommunication network 104.

As referred to herein, an immutable storage medium includes a subset ofstorage media characterized by the presumed difficulty, after the fact,of altering or deleting data stored therein. In other words, it isconsidered infeasible to alter data stored on such immutable media,without invalidating the data or the medium itself. Blockchain,newspapers and write once read many (WORM) storage media are commonlyreferenced members of this set.

An immutable storage medium makes use of public and private keys inorder to form a digital signature to ensure security. With the propertyof immutability embedded in blockchain, as an illustrative, non-limitingexample of an immutable storage medium, it becomes easier to detecttampering of any data. Blockchains are considered tamper-proof as anyunilateral change in even one single block can be detected. In advancinga transfer, the sender uses their private key and announces thetransaction information over the network. In such an example immutablestorage medium, a block, or blockchain node, is created containinginformation such as digital signature, timestamp, and the receiver'spublic key. Once the majority of nodes in the network come to aconsensus and agree to a common solution, the block is time stamped andadded to the existing blockchain. This block can contain data records asthe subject of the transfer. After a new block is added to the chain,the existing copies of blockchain are updated for all the nodes on thenetwork. Blockchains are decentralized in nature, with no single personor group having authority to unilaterally enact changes to data recordsonce created. Hashes can be applied to detect tampering, so any changein the data record will lead to a detectible change in the hash. Inembodiments, the immutable storage medium can comprise a private or apublic blockchain, and a data record of the sentiment block digitalasset can be accorded a unique identifier of a location within theimmutable storage medium.

FIG. 9 illustrates an example embodiment 900, as pertains morespecifically to sentiment-based tokenization application module 805 insome embodiments, for generating and deploying a sentiment-based digitalasset.

At block 901, in an onboarding and application initiation process, auser of subscriber client device 102 downloads and installssentiment-based tokenization application module 805 and initiates asentiment block in a designated user space allocated on a blockchainledger hosted on an immutable medium. In embodiments, a user as referredto herein means a content creator who creates sentiment-based content.Such sentiment-based created content and inherent intellectual propertyrights associated therewith may constitute basis for the sentiment blockdigital asset as referred to herein, and the benefit of the inherentintellectual property rights may inure to such user of a sentiment-baseduser engagement network system as described with regard to FIGS. 1-11herein. In embodiments, the user links their created sentiment-basedcontent such as (but not limited to) social media commentaries, reviewof websites, products and forum interactions based on their userprofiles or accounts, for access by tokenization application 805. Inembodiments, the user sets up consent rules for making theirsentiment-content selectively available only to certain buyers orclasses of buyers. The consent rules may include blacklists of buyersand certain classes of buyers who are prohibited from access and usageof a given user's created content.

At block 905, the network 100, for instance via server computing device101, may monitor continuously for new sentiment utterances and contentrelated to a given user profile or account. the user may add newsentiment blocks in accordance with new or modified content created.Tokenization application 805, in embodiments, processes the new usercontent for sentiment expressions. Server computing device 101 maynotify the user of client subscriber device 102 of new sentiment blockscreated and render a request for the user to decide whether to add thenew sentiment block to an existing blockchain. In an embodiment, theuser may decline the request, or defer the decision to a later date.Optionally, the user sets up consent attributes for each specificsentiment block approved. In certain edge computing embodiments, clientsubscriber device 102 may execute some of the actions described hereinas being performed by server computing device 101.

At block 910, in managing consent rules related to a sentiment block, ora group of blocks, the user modifies bulk or individual blocksappropriately for consent, for example whether to monetize block(s)exclusively available to specifically named buyers.

At block 915, in actions associated with monetization of their sentimentbased blocks now tokenized on the blockchain in embodiments, the userlinks their financial accounts, such as a bank account or a PayPalaccount, to their digital asset tokenization profile. In this manner,the user can be compensated via automatic monetary deposits by way ofmonetizing in selling their rights or licensing usage of their rightsintellectual property associated with the sentiment blocks.

FIG. 10 illustrates, in an example embodiment, a method of operation ingenerating a tokenized digital asset in accordance with asentiment-based digital asset and deploying same within a blockchainsystem.

At 1001, a client subscriber node, such as client subscriber device 102raises a sentiment block transaction request related to new sentimentcontent created or generated. At 1002, the transaction is represented asa digital token manifestation of the sentiment block, and at 1003 thedigital token representing the sentiment block is broadcasted to allnodes of the blockchain network, then at 1004 is validated by the nodes.As used herein, a token means a digital representation of an interest inan asset that, in some embodiments herein, may be used to facilitatetransactions on a blockchain. At 1005, a new block is added to theexisting blockchain, forming an updated blockchain by way of completingthe tokenization of the sentiment block by way of a completedtransaction for the new sentiment content.

In embodiments, a sentiment block, or sentiment block digital asset asvariously referred to herein, as configured may include attributes suchas: sender's address: the address of the sender of the transaction,recipient address: the address of the recipient of the transaction, usermetric: all metrics will be key/value pairs. key will be type(sentiment, engagement etc) and value is calculated score for the type,key contexts: list of sentimental context phrases, channel: where didthe interaction occur—facebook, instagram etc., username: the userid onthe network, text: text string which captures or encodes the sentimentintensity, image/video: url of image related with the sentimentintensity, metric timestamp: timestamp when the metric was recorded, andblock timestamp, a message record as to which block a given transactionwas recorded in, as well as its timestamp. since the blockchain ispublic, anyone can see all transactions on it. therefore thisinformation can be used to see when payments were made to contentcreators or wallets were funded for specific periods of time.

FIG. 11 illustrates, in an example embodiment, a method of operation inrendering a tokenized digital asset.

At step 1110, monitoring, at client subscriber device 102 that iscommunicatively coupled within communication network 104, generation ofat least one sentiment expression in accordance with social mediacontent data, the sentiment expression rendered in association with asentiment community of interest within cloud networked system 100.

At step 1120, generating a sentiment block digital asset (also referredto herein as a “sentiment block”) in accordance with the sentimentexpression.

At step 1130, rendering, as a tokenized digital asset, a digital tokendata file that is a unique representation of the sentiment block digitalasset. As used herein, a token means a digital representation of aninterest in an asset that, in some embodiments herein, may be used tofacilitate transactions on a blockchain. In some embodiments,associating the sentiment block with a given unique user account or userprofile within network system 100 with particular data contentattributes (such as, but not limited to, a date and time of the contentcreation by a given subscriber/user, an event, a product name, an entityor company name that the created content pertains to) may be sufficientto establish the unique representation or unique correspondence betweenthe tokenized digital asset and a particular sentiment block.

In some examples, the social media content data comprises one or more ofa hashtag, a twitter handle, an emoticon, a message exchange, at leastone of an audio and a visual content portion, at least a portion of awebsite content, a product name, a product feature, a third-partycharacter or entity name, and a third-party character or entityattribute.

In some embodiments, the sentiment block digital asset comprises one ormore of an image, a video, a text portion, a website content portion, anaudio rendering, and a text string produced via a speech to textconversion of at least a portion of the audio rendering.

In some embodiments, the sentiment block digital asset includes a useraccount, a set of consent rules, one of a pointer and a link to adesignated storage space within an immutable storage medium, anassociated monetary value and a link to one or more financial paymentaccounts associated with the user account. In some aspects, the set ofconsent rules may restrict one or both of access and usage to arestricted class of potential third party buyers of content of thesentiment block digital asset. Yet further, the set of consent rules mayrestrict a context of usage and/or a type of usage pertaining to thesentiment block digital asset.

In some embodiments, the method further includes rendering aconfirmation, at a display interface of a client subscriber device 102,of the tokenized digital asset and soliciting a decision to transmit thetokenized digital asset to the designated storage space within theimmutable storage medium. In embodiments, the immutable storage mediumcomprises a blockchain configuration.

In some embodiments, the sentiment expression comprises a sarcasmsentiment, and the method further includes modifying, responsive todetecting the sarcasm sentiment as being one of above and below asarcasm sentiment likelihood threshold, a sentiment classificationassociated with the at least one sentiment expression.

It is contemplated that embodiments described herein extend toindividual elements and concepts described herein, independently ofother concepts, ideas or system, as well as for embodiments to includecombinations of elements recited anywhere in this application. Althoughembodiments are described in detail herein with reference to theaccompanying drawings, it is to be understood that the invention is notlimited to those precise embodiments. As such, many modifications andvariations will be apparent to practitioners skilled in this art.Accordingly, it is intended that the scope of the invention be definedby the following claims and their equivalents. Furthermore, it iscontemplated that a particular feature described either individually oras part of an embodiment can be combined with other individuallydescribed features, or parts of other embodiments as described herein.Thus, the absence of describing combinations should not preclude theinventors from claiming rights to such combinations.

What is claimed is:
 1. A method, performed in a processor of a computingdevice, of valuing a digital asset, the method comprising: monitoringgeneration of at least one sentiment expression in accordance withsocial media content data, the at least one sentiment expressionrendered in association with a sentiment community of interest;generating a sentiment block digital asset in accordance with the atleast one sentiment expression; and rendering, as a tokenized digitalasset, a digital token data file that is a unique representation of thesentiment block digital asset.
 2. The method of claim 1 wherein thesocial media content data comprises one or more of: a hashtag, a twitterhandle, an emoticon, a message exchange, at least one of an audio and avisual content portion, at least a portion of a website content, aproduct name, a product feature, a third-party character name, and athird-party character attribute.
 3. The method of claim 1 wherein thesentiment block digital asset comprises at least one of: an image, avideo, a text portion, a website content portion, an audio rendering,and a text string produced via a speech to text conversion of at least aportion of the audio rendering.
 4. The method of claim 3 wherein thesentiment block digital asset further comprises at least one of: a useraccount, a set of consent rules, one of a pointer and a link to adesignated storage space within an immutable storage medium, anassociated monetary value and a link to one or more financial paymentaccounts associated with the user account.
 5. The method of claim 4wherein the set of consent rules restrict at least one of access andusage to a restricted class of potential third party buyers of contentin accordance with the sentiment block digital asset.
 6. The method ofclaim 4 wherein the set of consent rules restrict at least one of acontext of usage and a type of usage pertaining to the sentiment blockdigital asset.
 7. The method of claim 1 further comprising: rendering aconfirmation, at a display interface of a client subscriber device, ofthe tokenized digital asset; and soliciting a decision to transmit thetokenized digital asset to the designated storage space within theimmutable storage medium.
 8. The method of claim 7 wherein the immutablestorage medium comprises a blockchain configuration.
 9. The method ofclaim 1 wherein the at least one sentiment expression comprises asarcasm sentiment.
 10. The method of claim 8 further comprisingmodifying, responsive to detecting the sarcasm sentiment as being one ofabove and below a sarcasm sentiment likelihood threshold, a sentimentclassification associated with the at least one sentiment expression.11. A computing system comprising: a processor; a memory storing a setof instructions, the instructions when executed in the processor causingoperations comprising: monitoring generation of at least one sentimentexpression in accordance with social media content data, the at leastone sentiment expression rendered in association with a sentimentcommunity of interest; generating a sentiment block digital asset inaccordance with the at least one sentiment expression; and rendering, asa tokenized digital asset, a digital token data file that is a uniquerepresentation of the sentiment block digital asset.
 12. The computingsystem of claim 11 wherein the social media content data comprises oneor more of: a hashtag, a twitter handle, an emoticon, a messageexchange, at least one of an audio and a visual content portion, atleast a portion of a website content, a product name, a product feature,a third-party character name, and a third-party character attribute. 13.The computing system of claim 11 wherein the sentiment block digitalasset comprises at least one of: an image, a video, a text portion, awebsite content portion, an audio rendering, and a text string producedvia a speech to text conversion of at least a portion of the audiorendering.
 14. The computing system of claim 13 wherein the sentimentblock digital asset further comprises at least one of: a user account, aset of consent rules, one of a pointer and a link to a designatedstorage space within an immutable storage medium, an associated monetaryvalue and a link to one or more financial payment accounts associatedwith the user account.
 15. The computing system of claim 14 wherein theset of consent rules restrict at least one of access and usage to arestricted class of potential third party buyers of content inaccordance with the sentiment block digital asset.
 16. The computingsystem of claim 14 wherein the set of consent rules restrict at leastone of a context of usage and a type of usage pertaining to thesentiment block digital asset.
 17. The computing system of claim 11further comprising instructions executable in the processor to performoperations including: rendering a confirmation, at a display interfaceof a client subscriber device, of the tokenized digital asset; andsoliciting a decision to transmit the tokenized digital asset to thedesignated storage space within the immutable storage medium.
 18. Thecomputing system of claim 17 wherein the immutable storage mediumcomprises a blockchain configuration.
 19. The computing system of claim1 wherein the at least one sentiment expression comprises a sarcasmsentiment, and further comprising instructions executable in theprocessor to perform operations including modifying, responsive todetecting the sarcasm sentiment as being one of above and below asarcasm sentiment likelihood threshold, a sentiment classificationassociated with the at least one sentiment expression.
 20. Anon-transitory, computer readable medium storing instructions, theinstructions being executable in a processor, the instructions whenexecuted in the processor causing operations comprising: monitoringgeneration of at least one sentiment expression in accordance withsocial media content data, the at least one sentiment expressionrendered in association with a sentiment community of interest;generating a sentiment block digital asset in accordance with the atleast one sentiment expression; and rendering, as a tokenized digitalasset, a digital token data file that is a unique representation of thesentiment block digital asset.